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Record W2102141608 · doi:10.1144/geochem2011-106

The ‘rgr’ package for the R Open Source statistical computing and graphics environment - a tool to support geochemical data interpretation

2013· article· en· W2102141608 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2013
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGraphicsComputer scienceInterpretation (philosophy)Computer graphics (images)R packageOpen sourceComputational statisticsComputational scienceOperating systemProgramming languageMachine learningSoftware

Abstract

fetched live from OpenAlex

The development of interactive computer graphics to support applied geochemistry over the last 40 years at the Geological Survey of Canada (GSC) is briefly discussed. The loss of an interactive computing environment, IDEAS, in 1995 based on a DEC VAX computer largely negated nine years of work, though the experience gained was invaluable. The availability of the commercial S-PLUS package in a Windows PC environment led to the redevelopment of most of the functionality of IDEAS in the S language for statistical analysis and graphics. In 2006 a request from a sister federal government department for the S-PLUS software led to the decision to translate the S functions into R, an Open-Source implementation of the S language, and therefore free to the user. Since that time all development has been in R, resulting in the 2007 release to the public of a package of tools, ‘rgr’, to assist applied geochemists in interpreting their data. Subsequently, ‘rgr’ has been updated and extended. The move to Open Source R and the release of the ‘rgr’ package on the Comprehensive R Archival Network (CRAN) has made these tools, and their documentation, available for Windows, Unix and Mac computing environments. The paper outlines the features of ‘rgr’ and illustrates key graphic and tabular displays. Its functionality is reviewed in the context of earlier GSC interactive graphics packages. Supplementary Material: this is available at http://www.geolsoc.org.uk/SUP18713

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.259
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it